Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	21,048
2	New Jersey	20,093
3	Louisiana	18,611
4	Arizona	18,510
5	Rhode Island	16,718
6	Massachusetts	16,333
7	District of Columbia	15,693
8	Florida	14,702
9	Delaware	13,467
10	Connecticut	13,393
11	Mississippi	13,371
12	Maryland	12,610
13	Illinois	12,595
14	Alabama	12,458
15	South Carolina	12,446
16	Iowa	11,858
17	Nebraska	11,465
18	Georgia	11,382
19	Texas	10,708
20	Nevada	10,396
21	Arkansas	10,310
22	Tennessee	10,224
23	Utah	9,952
24	California	9,231
25	North Carolina	8,940
26	Virginia	8,720
27	South Dakota	8,697
28	Indiana	8,231
29	Pennsylvania	8,058
30	Michigan	8,008
31	Minnesota	7,870
32	New Mexico	7,696
33	Wisconsin	7,447
34	Idaho	7,420
35	Kansas	7,301
36	Colorado	6,740
37	North Dakota	6,130
38	Washington	6,075
39	Ohio	6,039
40	Oklahoma	5,923
41	Missouri	5,311
42	Kentucky	4,838
43	New Hampshire	4,514
44	Wyoming	3,500
45	Puerto Rico	3,310
46	Oregon	3,204
47	Alaska	2,774
48	Maine	2,676
49	West Virginia	2,598
50	Vermont	2,123
51	Montana	2,119
52	Hawaii	911

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Florida	517
2	Arizona	495
3	Louisiana	472
4	Texas	422
5	South Carolina	382
6	Alabama	376
7	Nevada	370
8	Mississippi	349
9	Idaho	322
10	Tennessee	303
11	Georgia	279
12	Arkansas	240
13	California	240
14	Oklahoma	227
15	North Carolina	191
16	Iowa	188
17	Utah	169
18	Wisconsin	157
19	Missouri	152
20	New Mexico	134
21	Nebraska	126
22	Washington	119
23	Maryland	116
24	Kansas	115
25	Kentucky	111
26	Montana	111
27	Virginia	108
28	Ohio	106
29	Indiana	103
30	North Dakota	98
31	Minnesota	93
32	Colorado	87
33	Illinois	87
34	Michigan	87
35	Alaska	85
36	Oregon	85
37	Delaware	80
38	District of Columbia	80
39	Pennsylvania	72
40	Rhode Island	70
41	Wyoming	70
42	South Dakota	64
43	West Virginia	63
44	Puerto Rico	58
45	New York	43
46	New Jersey	37
47	Massachusetts	36
48	Connecticut	22
49	New Hampshire	17
50	Hawaii	16
51	Vermont	12
52	Maine	9

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,763
2	New York	1,651
3	Connecticut	1,231
4	Massachusetts	1,215
5	Rhode Island	932
6	District of Columbia	813
7	Louisiana	749
8	Michigan	635
9	Illinois	588
10	Maryland	553
11	Pennsylvania	549
12	Delaware	535
13	Mississippi	439
14	Indiana	415
15	Arizona	344
16	Colorado	303
17	New Hampshire	290
18	Georgia	286
19	Minnesota	277
20	New Mexico	268
21	Ohio	265
22	Alabama	250
23	Iowa	247
24	Virginia	235
25	Florida	217
26	South Carolina	207
27	Nevada	203
28	Washington	195
29	California	189
30	Missouri	186
31	Nebraska	158
32	North Carolina	154
33	Kentucky	151
34	Wisconsin	144
35	South Dakota	129
36	Texas	128
37	North Dakota	122
38	Tennessee	115
39	Arkansas	112
40	Oklahoma	110
41	Kansas	106
42	Vermont	89
43	Maine	84
44	Utah	72
45	Idaho	63
46	Oregon	59
47	West Virginia	55
48	Puerto Rico	53
49	Wyoming	41
50	Montana	32
51	Alaska	20
52	Hawaii	14

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	11
2	Alabama	7
3	Florida	6
4	Mississippi	6
5	South Carolina	6
6	Louisiana	4
7	Texas	4
8	California	3
9	Nevada	3
10	New Jersey	3
11	District of Columbia	2
12	Georgia	2
13	Idaho	2
14	Iowa	2
15	Massachusetts	2
16	New Mexico	2
17	North Carolina	2
18	Pennsylvania	2
19	South Dakota	2
20	Tennessee	2
21	Washington	2
22	Arkansas	1
23	Colorado	1
24	Connecticut	1
25	Delaware	1
26	Illinois	1
27	Indiana	1
28	Kentucky	1
29	Maryland	1
30	Minnesota	1
31	Missouri	1
32	Nebraska	1
33	Ohio	1
34	Oklahoma	1
35	Rhode Island	1
36	Utah	1
37	Virginia	1
38	Wyoming	1
39	Alaska	0
40	Hawaii	0
41	Kansas	0
42	Maine	0
43	Michigan	0
44	Montana	0
45	New Hampshire	0
46	New York	0
47	North Dakota	0
48	Oregon	0
49	Puerto Rico	0
50	Vermont	0
51	West Virginia	0
52	Wisconsin	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	135,413	1	99
Lake	Tennessee	99,487	2	99
Lee	Arkansas	96,421	3	99
Dakota	Nebraska	93,079	4	99
Buena Vista	Iowa	88,940	5	99
Davidson	Tennessee	23,164	126	95
Richland	South Carolina	13,349	417	86
Orange	California	8,787	780	75
York	South Carolina	7,609	915	70
Pierce	Washington	4,293	1545	50

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,902	1	99
Randolph	Georgia	3,688	2	99
Terrell	Georgia	3,399	3	99
Early	Georgia	3,042	4	99
Emporia city	Virginia	2,806	5	99
Richland	South Carolina	245	673	78
Davidson	Tennessee	230	708	77
Orange	California	147	963	69
Pierce	Washington	126	1061	66
York	South Carolina	46	1691	46

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.